Q-Learning with Scalar Adjoint Matching
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Source roundup from published reports. Claims below are attributed to their publishers, not independently verified. arXiv Robotics — research abstracts: Q-Learning with Scalar Adjoint Matching. This research proposes SQAM, a reinforcement learning method that combines scalar adjoint matching with value penalties to improve fine-tuning of flow policies. By reducing the computational cost of vector-Jacobian produ…
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- arXiv Robotics — research abstractsQ-Learning with Scalar Adjoint Matching
This research proposes SQAM, a reinforcement learning method that combines scalar adjoint matching with value penalties to improve fine-tuning of flow policies. By reducing the computational cost of vector-Jacobian products, SQAM achieves performance gains of 18 to 35 percentage points over strong baselines in four hard OGBench domains. The method is also tested on a real bimanual robot, showing improvements over supervised fine-tuning.
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